package net.wlm.jtp.dws.log.job;
import com.alibaba.fastjson.JSON;
import net.wlm.jtp.dws.log.bean.PageViewBean;
import net.wlm.jtp.dws.log.function.PageViewBeanMapFunction;
import net.wlm.jtp.dws.log.function.PageViewReportReduceFunction;
import net.wlm.jtp.dws.log.function.PageViewReportWindowFunction;
import net.wlm.jtp.dws.log.utils.JdbcUtil;
import net.wlm.jtp.dws.log.utils.KafkaUtil;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import java.time.Duration;
/**
 * 当日APP流量日志数据进行实时汇总统计，按照分钟级别窗口汇总计算
 * 粒度：ar地区、ba品牌、ch渠道、is_new新老访客，
 * 指标：PV（页面浏览数）、浏览总时长、UV（唯一访客数）、SV（会话数）
 */
public class JtpTrafficPageViewMinuteWindowDwsJob {
    public static void main(String[] args) throws Exception{
        // 1.获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        // 2.数据源
        DataStream<String> pageStream = KafkaUtil.consumerKafka(env, "dwd-traffic-page-log");
        //pageStream.print("kafka");
        // 3.数据转换-Transformation
        DataStream<String> resultStream = handle(pageStream);
        //resultStream.print("result");
        // 4.数据输出-Sink
        JdbcUtil.sinkToClickhouseUpsert(
                resultStream,
                "INSERT INTO jtp_log_report.dws_log_page_view_window_report(\n" +
                        "    window_start_time, window_end_time,\n" +
                        "    brand, channel, province, is_new,\n" +
                        "    pv_count, pv_during_time, uv_count, session_count,\n" +
                        "    ts\n" +
                        ")\n" +
                        "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)"
        );
        // 5.触发执行
        env.execute("JtpTrafficPageViewMinuteWindowDwsJob");
    }
    /**
     * 计算浏览日志数据进行汇总计算
     * @param pageStream
     * @return
     */
    private static DataStream<String> handle(DataStream<String> pageStream) {
        // 1.按照mid分组，求uv，使用状态State记录今日是否第一次访问
        KeyedStream<String, String> midStream = pageStream.keyBy(
                json -> JSON.parseObject(json).getJSONObject("common").getString("mid")
        );
        // 2.封装实体类
        DataStream<PageViewBean> beanStream = midStream.map(new PageViewBeanMapFunction());
        // 3.事件时间字段和水位线
        SingleOutputStreamOperator<PageViewBean> timeStream = beanStream.assignTimestampsAndWatermarks(
                WatermarkStrategy.<PageViewBean>forBoundedOutOfOrderness(Duration.ofSeconds(0))
                        .withTimestampAssigner(
                                new SerializableTimestampAssigner<PageViewBean>() {
                                    @Override
                                    public long extractTimestamp(PageViewBean element, long recordTimestamp) {
                                        return element.getTs();
                                    }
                                }
                        )
        );
        // 4.分组keyBy:ar地区,ba品牌,ch渠道,is_new新老访客
        KeyedStream<PageViewBean, String> keyedStream = timeStream.keyBy(
                bean -> bean.getBrand() + "," + bean.getChannel() + "," + bean.getProvince() + "," + bean.getIsNew()
        );
        // 5.开窗:滚动窗口，滚动窗口大小为1分钟
        WindowedStream<PageViewBean, String, TimeWindow> windowedStream = keyedStream.window(
                TumblingEventTimeWindows.of(Time.minutes(1))
        );
        // 6.聚合：对窗口中的数据进行聚合
        // DataStream<String> reportStream = windowedStream.apply(new PageViewWindowFunction());
        DataStream<String> reportStream = windowedStream.reduce(
            // 增量计算                     // 窗口计算：添加窗口信息
            new PageViewReportReduceFunction(),new PageViewReportWindowFunction()
        );
        // 7.返回
        return reportStream;
    }
}